{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <div style='color:#ff9702;font-weight:800;'>矢量化环境</div>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nes_py.wrappers import JoypadSpace\n",
    "import gym_super_mario_bros\n",
    "from gym_super_mario_bros.actions import SIMPLE_MOVEMENT\n",
    "import time\n",
    "from matplotlib import pyplot as plt\n",
    "from gym.wrappers import GrayScaleObservation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "env = gym_super_mario_bros.make('SuperMarioBros-v0')\n",
    "env = JoypadSpace(env, SIMPLE_MOVEMENT)\n",
    "env = GrayScaleObservation(env,keep_dim=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 一、矢量化环境的作用\n",
    "\n",
    "https://stable-baselines3.readthedocs.io/en/master/guide/examples.html\n",
    "\n",
    "![](image/矢量化环境/1646580687830.png)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](image/矢量化环境/1646580933029.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 二、DummyVecEnv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](image/矢量化环境/1646581131767.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from stable_baselines3.common.vec_env import DummyVecEnv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "env = DummyVecEnv([lambda: env])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 240, 256, 1)\n"
     ]
    }
   ],
   "source": [
    "state = env.reset()\n",
    "print(state.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(state)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x211883cd7f0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# print(state[0])\n",
    "plt.imshow(state[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 三、VecFrameStack"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](image/矢量化环境/1646590058323.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from stable_baselines3.common.vec_env import VecFrameStack"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "env = VecFrameStack(env,4,channels_order='last')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 240, 256, 4)\n"
     ]
    }
   ],
   "source": [
    "state = env.reset()\n",
    "print(state.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(state)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(state[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 0 0 ... 0 0 0]\n",
      " [0 0 0 ... 0 0 0]\n",
      " [0 0 0 ... 0 0 0]\n",
      " ...\n",
      " [0 0 0 ... 0 0 0]\n",
      " [0 0 0 ... 0 0 0]\n",
      " [0 0 0 ... 0 0 0]]\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(state[0][:,:,0])\n",
    "print(state[0][:,:,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[140 140 140 ... 140 140 140]\n",
      " [140 140 140 ... 140 140 140]\n",
      " [140 140 140 ... 140 140 140]\n",
      " ...\n",
      " [214 124 124 ... 124 124   0]\n",
      " [214 124 124 ... 124   0   0]\n",
      " [124   0   0 ...   0   0 124]]\n"
     ]
    },
    {
     "data": {
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id=\"patch_5\">\r\n    <path d=\"M 33.2875 227.986219 \r\nL 265.2235 227.986219 \r\n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\r\n   </g>\r\n   <g id=\"patch_6\">\r\n    <path d=\"M 33.2875 10.546219 \r\nL 265.2235 10.546219 \r\n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\r\n   </g>\r\n  </g>\r\n </g>\r\n <defs>\r\n  <clipPath id=\"p6fd361d8d6\">\r\n   <rect height=\"217.44\" width=\"231.936\" x=\"33.2875\" y=\"10.546219\"/>\r\n  </clipPath>\r\n </defs>\r\n</svg>\r\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(state[0][:,:,3])\n",
    "print(state[0][:,:,3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x1152 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,16))\n",
    "for idx in range(state.shape[3]):\n",
    "    plt.subplot(1,4,idx+1)\n",
    "    plt.imshow(state[0][:,:,idx])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_img_4(state):\n",
    "    plt.figure(figsize=(20,16))\n",
    "    for idx in range(state.shape[3]):\n",
    "        plt.subplot(1,4,idx+1)\n",
    "        plt.imshow(state[0][:,:,idx])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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ZnhwTK8lYGusapynr+/nNA8VSH4wMCfjRWFcDgCSe33lw4dyyKAwCHDUORqG9UNqZi7BnoHvedEIku1I9raWjU2HyzAVj1Qwo7xGkW2mpHv63DzcslU2D/w4DTZdExJo3gQaDrPV8z181dYD7QXSYzwKwHwcsVUnLvifM8NswrNi8KAketI69FYlG4Z06AJR+d5s0YnZl2BlPdlIqod9KYblczco/meKsdFkpCOhpH7YgMd041qYXZodGujsJUl+lwWch/ZT8fy8QAoHh1H+ui6hBZFJeuXW+i+bI6RFU3HFT06W8mkUfBYOZ0LctBMRl4VV/Ax1FB0GbJf15A9XvoSjAklk2OUtpJE2GEbWeva6BwXY5SjR/W1eCPR22Wj6IlSOhYbjyyTS6Nj8uhaOUXSyf6fT+i61FhrlVWd7ozRtbCKopH12/10nTMeXe73l3t0LZwuQ/5LDlyxVtxRxv0U52htJWJ3mEna3k1XntHzsnSrqKOsm64mO+OeraJ9fgYAJqdGpy6hjKJ1VjWJnAd30nnhVABkr07buNH5y2xSSf71gK8lnYA5h+HQFZ0xq5rpKYrDazPqhezWRidmZwJ0jtOwNdjxRBmVcuzvq+lVR+4aeCLNJE5rpOb8FBxphm3uGg+dnaNoWxWFCwr2sfHKeXSOMZwbc0gm4qgEk0c2vzxzO89ddx7tEwxbc5fi2zQyEpMy66i8LJ/eDN03wVdwz54RrAycKSo1S00oTnwTVoW/LsapBd8MMxhXtAwzO6nW4nAmGv4q+JuDpt5RDBEsZs4oOsL2lVPozjVsk7bL2OpkCFys8EeSuTJrG89dex7t440825oVY9PI10a+9ZKsUrZcNZ3OAozVCFVizANHRjYE9HQnVedEoJl1VGu/r3+yF00f+QHmipXomunEFRuJK84oc+GTrbS5RvaXFmZm4sSjVF2cR0+WYZuxSUdrtqGkOI5vLEtclrOLl75+Jh1FRp4jjirElGqwdMRbc0HmPv7zjSW+SVzFJfk2jYxEQkYHVRcl4Y7V0fqXJ/Pv3jG6CTCAyLRun5BPlPSITp+QT5RIxekT8qehfeKnt+0s8gadqR8NUlZv0Jnn0ZAR0+kT8okSY3b4hHxC6BoKmk/In4b28ToBC/ujxJXr9An5RElO7PQJ+URJDev0CflEscoen5A/DccamU9Db77HJ+RjiClMgYGYzf6vR8p69gF95bThpyzXvjqPmBIN+w11TI8biNh59Y25eOJUVszYQ5gSuEYIsL5yPJm/1Kn+mcTynIO+z/+9YyaSQ2H69FJywwPXCAG6vDaqlsgc+b+xXDpxYJ/uO7Vj6N4fT/zUJk5LDly/Psb+08I4/OgEVs4ciKqp7Yth15vjUMf3cGFhYETMMfbePImKi8JZsnQ3USYnAJou8ca/5+IodLFyys5hbTc8Nw/FpZN6ZSXjohp8n699bR7ONA8XztiFeZi9iy/vnU7+Ko3WuxyckTEQTfby5tkgwfzpR0i1BR8mVPTG41gpU/xIGhcXDQwH1pZNxFsRQca0OmbGDz+5snemTPHT0xhcF450J1P8bh7KxE7OzT04rO2BK/I4cnMiK07bgVU2Zt1dmom3X55FX5GTSyftGtb2g4fn4EiWKbr4CNn2gT31616eR1+Oh5Uztg9r+8r7c8h4W8N9c6tfXVj99lzUCJUzpx8gxhy8u7ytJZvw70Lpb6K4YMxAXXjlwFSkJiuF06uYEF0f1NajKxyeA0eenuJXF7a1ZFP/SSr2Se2cmTn8EOPA8kQO/TyHS+ZsR+7vyXR47Gx+bQru8X1cPG73sLbbfjKTxllmZi3fT7J1YJ/36y/NQ8r93e/1wicbAox6ixLpubGD7gPx5P9qL3JCHLp5YIhdcXUqnkiNwlXt4PIXsx4RxpE7bWQkt2M/rxYlKQE9bGB83XBmCl0FOtmve7BWBQZFHPp+Ijef9jYbJ0aiJCdB5MD4p3dcIrWLZOL3SsR/FJjv4htTuHn5m2yYk4pkt0P0wIy5Fm2n9MpIbM0yWS8H/lDVF6Uy/7JdVNwxFtPhKqToKN9kDpJE8Y1JxtbJPwfet3N6Mso3G2nfmErGE/uQ42PRTQOTIWXXp6BadQqfagbNv1vnTYyi8lZQTBrZXy9GSUxAtw3Mztaen0pvukbey32Ymods3pFlDn0/jpUzt7N3BpiSk9AjBuYjOqcn0zgbkrfqRO9qCsj34e8lc9dZ63htYhJKfJyfv7zJ0ZRfbCO8WiZ9XaC/Kq9IZcUlWzhwdQE0t0HMoHlcRaH42wkoTon85wL91XJaCgnfOErzX7JJ/PcBpPhYdGWgd1DyrRR0k07hk02g+3cnXVlxNPw/J44WO0W37UNOTEC3DsxHHP1aKq54jYK/dSN3DxGzxcyhu6KYV1RG68IuTMmJ6OEDwRMtC1Jom6STsUnFfsQ/fBLg8B1J/GDJWlaPTwyom67sOI4uNxNdIpH8dmCZy65P4ZrzN7FlWTa61+vnL91mpeS6GMxdMrkvBvq6flkqY68+TN1DBUS8X4IUE+Xnr+IbU5B+tW+F/vxrgSP2yEpjdi3sokZatif7pRX8uRatuZX4DWa2vj8OecgSqMkhkflmJwVPlLLhTf+tMOnvurF+eIAjT45HdypYmgPn4PJ/f5gVm0v531fOC8hTwnPbaLh5NvrSdpz7YwJsC56uYeJ/qln9xjy/zehKn0Tm/VvQ500m7rfV7No0NsA2640+6u7w4GgOx9IyZIvfTz9GiY9j1tsN/GNN4P7n2EM63Vkyypx2XEPylf+HUnSHg/Hv97HmzTkBARyWTonEXS5S7itnx6Yi//uu7UXeXUztP3LpqY7C3OnfHZY0yH+8nMUbS/nzf/z3scce0on5106O/ngm6rgepNLASaH8x8o48+1Snnz17CEhkBJpv9uC69yZ2O6qo+KjrADb3Je7cD3Uw9GDqZi7B80e65Dz0y2YMjMY/2otr70+N8A2aadG/TwZOcOBVOY/GZr3oLEra+6Hrby4ZlGAbVijRHijRvRNVZRtzvYvz9/b0Cuq8a6Jp2J3OsqQ/eKyRyL3b/VM/3cpL609zS8tebuKff0eSh6chh7rxlwdOMGb//vDXLC5mEdeuSAgT8l/2krH1bMxXR2oGYD8vzQTt6qFjzePQxkUbCZ7JbJ+8RHK+EIyn6vm3bemBtimfeCh8jJA1rHU+i805t63A8lmxRRn6kG1Gb+iGq5x5+nGOuojO84g/l0rFkX1pR+j+LvpFD7qIsrciWrX0N0SSHDbOesB2NuTQe3qNExyoG31Mgs5ziIkWYcwFdVmiOaas94nzmSEH679mREpMtS2Y6yEfPUsJBXCbS56+9Onzy1hYWwpAG88MxVNl1DDdL+9q6pNp+aeeWSu7yTO4vBdO7ygk2+N+QiAFw8sR9NUsKqoNn/RVPxmLnk/3UGsqXfAXzFe7py3wfBX5HLCa8BqUnEMyXfJXQWMuf8AUSYnqk1H0ozlvtuXGf5a3zQB70dJWBVvQJnLLw6nwJmHSVbRbRpq/37vG8/ZgE3y4tRNvPNgSlB/tUyVUNzTQIcwm8fnryUL9zE5wliHXf9IPiqSYTvIvM+mU3/nPBL2uIix9vmunTyxiSsyja74Kx+chao50K0aqsffX5X/M4/83x0m1jTgaz3Zxe3T3wHgcc8KZFXHbPHiGpLv0p9MJP/nO4kz9aKGGfnSLDp3nGFsN322dB7Ks1FYgtSvkmvjGPO0G6viRA3TfEHQtyx/AwWdRk8Uu55MM6K5htjWz1dId01Gl8Bs86L2944uXrqVLKvRg1z3q+C+7skC+cbZ2Np1Ii0uGvvTC6ZXsyLZeDitW30ami6hhWkwaFOMik7VL+aT91wNMeYBXyuZDm6Z+J5R5oYV4PWgRAapI7+aQe49HxtLU7oJ7l62hvfbC1k3xdgLO0bbQ9t1gRElxvd1v8zctnw9sqSxZvKxp5ELeUz/PyX8Koku66AM2J67eAfj7XWsOXsaav2xroke1BZJ9635AmROqefy9O389afnse7V/jhl71ECNmT347eOl+bk+1M38Ps1F7BupVHmKHUrHZcERngZtgMZ0Ww6P1i6ln/UzBrkr23U3TUnqK1u0kEeyNP3z11Dp2r32UITzEkKWmZd0dEH+fqKMzeTYWnjtQWFaN39oXXHGpAg/tIH+XrirArOSdzPv28+m+oP++OUvYHDnIEyD/zbmtvN98a9yx9fuJB1vzPybVF30Hdnzoi2aqTKDxe+zh8OLPWVOUvbSsX9x6lfx/4twd3L11DmTPLZJlOKY0Xwzc+64l83v33220QrDl6dnml0bQHlWKRusLo56Jm08LQDzIkq59UrFrLvoNFY6F5XUFujbsq+D2OL2vhm3mb+/MgFrFvV2G97AAi+I2/wHnI13sMP57zBg5vP9ZU5Sd1CxxMzg9v2+8ukIvP1JR+wemIKpiQLpQ8MGMTtg9b16cQNEwLpUk3cdebrrJ2ZieZ0UfbQgK2lQ0b9f2EsenR/0BBIXZO4a9YGXrtuMUe2a1Tel4kaltGfKLF25h5u/uStoCGQ6HBV1jaeevJ8Vj9WQ+93FRoG5VtedIDvfPS2EQIZZNVhQngtb+njWD0xhYxlXr8yj/lVBx0POmhwBwmBxNjCd9bsvayemEJ4DhwZZJvysUpzdDy2YUIgPbrCneesY/XEFCSzidIHpvnSwhpk3L8qYuqPSoOGQKqazPcXvMG6C2exrewoFfcX+X5EySshnRHONW+9HzQEUtLhO4Uf8pf7V7D6xRYa77TRd95AvqVpO7ljzzoeWb8i6E7GhbElfLJzDKu/lkLMJaqfv8Z+t55ZfykLHgIJRJscTCqqZvXEFHImeSkZZJu50UtldDimtOAhkB5d4Xtnv8HqiSkoEeGUPjDelxZZLuN6Jo/cYUIgvZrM9xevZ92iItTWNsoenOYXAql/LYIL//5J0BBISYNbJ23ipbvOoe5NB9X3xOG+diDfqydu44796wJOwgFAh/NT9vLYS+ez+oIU5Gvw85dySTkXvL6DNR/MDBrznmVtJSGxm9UTUxgzz+NfN1/oD4FM8CAHC4FcOzMTFIWyhyYEJOqyUX5dl4IG69ct9lKrplL26ynGE9HPI8bunD7VHNS28Fv7WUMqtXdF4rxsFkNrkWQy0amGBQ3ETnpiK2ueScN+iUbZQ7MYGqImmUy0ecMN2yH31XcfZO30dAomeSl9aBZD0RUFt1czntBDn9weN2unpyNF2Cl7aEoQf0lGF1oPtAXYMTccXQ2j7KEgrYpk7P5yDuOv1MvLWaOlcvTHKXgikwIuLskyXV5bUH9l/eYT1jyUhvcaKbi/zCY6vXbjvkNszW/vZO30dHLne/tth5TZpNDjsRqtmiT5h/S1t7N2ejpysjm4rczA9s8g/towOwVdVSl7aFqA7bHOl0dVgh8ksbyFNVoqFb8o6F+/9g8ZRZHpUa1B/TXmBztZ86M0Om4003NGoL9ki9nwVxDbyJe2seY/aSQvO+avwHrd7bGhK8bGmsHJ3soq1k5PJzFXpziovyRUVTJEGcRf8oXbj1Ly80Ahg7GoHVGr0b0jgfC8wCWR3A+g+PfTAoWMcexM/Wnh1P+qgBmzSwLSS56dQPEzE3EmBGk6JZ2Sn0xg29Jkrj/j3YDkppvm0P1qOs1DD7Drp/gnY9kz28JdZ60L6HFLU8eT+wGUXh58h1Dp1ZEk3+FhSmGVMeYabGu2cPaOBop/NCaobeNsSPnYS19dBHJ64JLI3K1dlDwYvHvYl6zRlWOi5elsCqYE7itueimHI49PwRMZ6C/dpFP6/3I5/LUsLlkUeADe0R/Ppv6fecNucCj55WTemxrB3WevCUjznDGd6LftHD03+O614huTkK5UWTFzT2AIZGwsCz9p58j3AltOgJqlCvn/dKPrElpC4PLmudtqKb0/iJCB7lwNTYGOtWkkjQuMbjC9GceRP0wNuhFFtelUrkzk6M35nLkgcFdf8e+mU/HC2GE3k5T8eirvz4rhe8sCQ0a7L5sF6xOoXRR83f7IHVnUL5O5YdF7AdGIppwsJm9xUvzN4Ec5VVxopfB3LuLje1AjP2UIpEAg+PIz6ri8cKub7qjBaz2jD3ELN7lRB9mqVgUY5tCnIZgl1d82zBx0XDccg59g0miCOwYhRXhRj60Vn+DOu/AwNx1Rg5Y2TmCHVbjZ5Vdm3Tx6W6vs9bP12ka7e99ohfxsHSfmLz3Si+rtt1FPzNZq9+CMOrGwyWNEWNw0DK6bptFfJ9Bfo/e1gu5vGy4fO7NxVGiRqq9eac4Ta1dNEZ6AoNhR/dImB8Ta+tAz2n2fufOTMEmBmxCGorhUXKqJ5EG2XZmJmK0u3M4RAg80nfK+BD/b1rZELF3SqEIg9/Zk+NtGROBMG40lVHdFk5bUgXdQtJJckA2MXGZTr0xSRA9W84C7tfx0zNLI4YAml44s6X757slJwG7ppf04dgCoKvXOaH9fZyThjdBHDoEE9vf6+6vRFIMjeeTgEICSrkTS0toG4plVGS0njdGEQKrdZrILWukNHwg9lAtykEfjrz6dcJPbL9+O3FgSlBG9heRR6fCE+dl2Zyahh40i1l7TOeRI9bNtcsdj7lJGFQJ5sDuVlIw245QVoMdpxZ0Zz2hCIDu67KQndOCMGZCvMiZvdGK2N+j0PpxB/L4GtGhjrNk800bsMFvlBmPqdrP3tXFk/fkweoaxdNU1Vic6rpOKuoTj2upeL29tn8TYO/YgFRkhffZkDxWXQ8wo8r3xSBEF1+xCnjIOgGirStW5dsaPYAfQURpH8v1lkBSHbjIqaN1Zowv0t9dKaOsTiavtQA83Zmmb5kZiG+6I00FY27yUvziG1NXlaMnGcU7tEyEropNago+lfLjcvPfhRMb8bC+MMTZT2DI81F0T/Djcobx+aAJjvrELebKxcSUqwkvFBWZyRmFbXJJG0Z37oSDLmAiTJGrPCH680lDsR01YnojA6nChhxmPnYbTY1CGnu0WhLBWjYanc4l/txot3pBR61QTKbbAI22HIjlcfLJhAnm/O4Cea4zrzXkqytIuevtGiAjUNdbvnczYG3cjTTDmUCJiPZRfCoHbRQLZciSfwm/tQp44BiSJGBNULwtjmNOG/JArwgj/STv2MBndbPRC6s+KHZ2Yu/IkuvIV2gszcKQeC4GsoPm7gcsvQ3ElhKHP7qS1biytU4w+Re4aF+X1I1RMQLKYuXDOTvYsmeY7syvmkETMTgmCz6n4ceeMjby64AxKLu6Pwe6SyX61E4KsKAxl0vQK2ufmUrdA8a17FtyzHW4c2barUEW1hqHND8MV3x+W9+uDdN8cuPwylN5UM+2z3ciegQmrgr91U9p2/AcfAPYwZs4rpu6sidQtNB5ASdtBORgB00doqSSZO2a8zWunLaXsQqMi25plMje44LTjmwKsmLGHQ/MmcvQcy6AQyEPwjZFtXRP7aG6JpjdN8k3wFfxkJ55bRq6e3RkKXfkarphs3xlnhU82Unl98HPtBqNF20mZW0/nOeNp7F+hytik0lERi3mkEEhF4ZuzPmTT6Qt8k4MRR2VSNmsQfD7Zj2/O/JD35s+l7CKjThghkDVw2ci2UVNaaZubStsEybeBJP/uLaOfALNM7vAJ+USZkNzgE/KJkmDuGfbwvdFQfsnIAhqOquX+GxhOBNfMHp+QT5TCrMZPHVqXGdbuE/KJYpa8VFx4YsfXDKbiAvOnDoFsO80VdKZ+NCQXNg97WOFIjI1p9An5RIlQnMPO8o+Gz1I3G5YE7n4Ts9kCQYhgeqr0NLKnDj/R0FyfQXidjm2Oi8RB32u4IJfS8l6yxzX4wriGUl6ZRNr7bio74/zuUdOdgdbjJTOvGXNa8Flth8cMHi//LBsYRahRKq1zwFZjxqZLaElu37uqwNhCF2UzQhYlReap4tPInjJw35aecJqaYmhtzDpumbXISPYUJ5AzptFXNk2X6Fw5nRfK2o9r23konfBqiegxPVgTOgbufdF4XirrImtS/bD+qlTSyHpTpdNl8/dXcwa9zW6ys5tR5OAtUEtPOLjcvFs3xs+2wpyGyaETYXORcJx8AwytC7Vt0bQ6I+npiD9umQmz8UbxeLLHD5TNoyq0rRjLy0cTj2vbtyUVpVwmbXYD5vSButCxchp/Lun0+/2GUtubQdwhDR387lG3PIPumj6yC5qG9Vd1cyzyv13sbMr0s63ypiOpOonRPVjjO4LaaroEms4LZXP8bCuT4/HuD0Nzhh3fXxYzfzsyy68u9LotNC1JZ2ON7bi27tcTaSs1kz2+wa9s3ZfPwRT/Wzul1w0/9g2zgakXGg4noUUOzM5a00HZH0FllhWGOY1D7jLRWWCiY184zYkDkz8mu05YnYlqPRGsw3SPNAnlrAS8uxXo735JfYoxHtOh+WAiDNoQYOqVaamJocVsfDd9RSqeHQrlOYM2h3glwhIl1D3xtKQMPxmVMNVMeKlEpZ4EgzbEFNS5aKiLxpQ5fJeuY7yO7IK2HjsRYQMTT83zvNAYjt0aPPYbQI930zTNhrMtEi124A2WjnEu6FNod4T5zZAPpeZrOXTVe5DSBvkl2YGn3UZzRwQxkcMfVOe5aQ4dtV5MgzZKWCxeuoq8SM1RkBh4bvoxmlYmorWp9ERbkQY9qJoXaFAXjZIxfNe7Y6aMLkNbr93PN80LNaiPxJo1fHllr7EHfEJsI0URA2GDmy910em2sTipBLsSfPKvITaaV26byZTIoyyIL/V9fnBxO5XdcUyJqyXdGnyeQdNlnvz9UvLD61iWPBDn3RQXxfaULNLDO5kSVR3UFuDxX51BZlQz56QcRO7fuubRTGy4qohws5tFCf5vRn1822KkbhMJ+W10n25F8moBdaF5kYY04a6H9fS32qhZPvKEwReCBKrV+NEkDWS38SPqCqgW4/+y1/g82D7sk0Xmuhaqz02gp2h4MQo+P6y1FqIqdOTLmlmePvzBCaHApsZCul9OxRkv4cgbvv7J3eM8VJ0fR+ZrI6+fft5oJtDnduIZ50D2GPtgdcV4lYnWv+9a9kBfngvmdOIN/oK+z0zWy3VUXiKE/GXC3Guc/hnqQgZYklyMLoNl+M4RADKSjiPPQ+VlSWS+2vj55O4ESI/uJCupDe+EgVetDn5vbV++i5yMFtKjO/3eo3uyyPpXNWXXpdKXI4T8ZaI3W8UVLfHxN6by1/WBBxiECs9/uJC/jbJ8xmy2pNOX6aHiiuQvpaCdXhNqfRjuWA3XWGPc15fpoS/Ni6nZQofj1DTJWf+oovQ7GbhSR/c2CcHnh25VaZ+kUnJ1NBHjRt7t9d/Ic9vmk7YJ4vbryG7oGH/8ejhor6KOM8ND5eXJX5out6RByf4MGg4nIWkgeSQsZYZwbXVmzF0KuqzTVR5Dyf4MFOfJu3fWP6oouSkTd5L3U6+dCk4xFg011oP5ldigcdz/jWxqLGTTjxYAIDkVFKdO4wKN1lkqWI4/KeS/zizr9GV5OHpJEhnrhz+B4vNC0oyg/cFB3Lqs447R0Cy6L8xUUsHWJAecRfZpyfp3LaU3ZuJJFEL+b6B5vpe0DzSe+2gUW9W+xOzqyMT+PRNNM8xs/MXCgQSzBqaRZ3cDN430j6Frl8WR/s7o3v5wKpG9YG0dkk3JeC2n4pIIa5IJa5IZ5rTfEybz1UbKr0nHnSyE/F+DolO9QifvJZUtM+xsmWFnQ33RyHZfMjQkJKeLvmwPHWMUcl/xUnXB6Otg8B1gkk73eDeNc6JJ/chx4q9A7MfWopP224/IfD342dijRfYae1fzZlWTvaCa/Kk15E+rIev0KlLOrsaZcBJEp0PGW+1UXZKMM8sthPzfhqxTfpXEkf+bxpH/m0b0TSpb23J8yS/9fTEeXUEbdFqFhuT3N8DLLyzmo6lWnttymi/txbWLqHNF4xkywzrU9i8HZvPRVCtrHlvk++5znyxgQ31R0HsPprIvHu9KlYP3poCk01Popvwqadg9HEFdcLzEjulu2gvDSNrl+tSC9mPI6S2jMlGgL0XDHa/6Ob+iKZ6y3RmU70lHM+moVoY7x29EJA3SPuyhbkksvQVi1vq/GkkHSefgTxKRvg77utIAuPTK91j9z4W89JIxM6whsacjg7++s9DfXoeWb88l/59e/rJnDh5d4arz3uO99dNY/c+FuPqPU+ny2njp34GzzNqCKegKvPaC8d3rZm+mtjyB1f9cyKFu42RPl2bipZcWBTwcfPn/lIwYltKywIPJZSb+kIe2IjOjeJdXUCQVknY6QYKm6bYTuo5m00nI6KBidzpavIcxmY1kJ7VR7k0gLNxNenQnnS4b3R8mIbs5IVFLKiTtctI8NZyuiULIocTB+9IoOvcI7e/aiTU5uOyKd31pNX2x7N+Wy7VnvY9Ds9DrtWKVPcaZdwqUXS0z9pE+1v1iAhfl7OXKiwZsNSTW/2cul698F4+u0OiKItHSjeo0odp02mZ5SN5k4pXXTuPKi97lugUf+uXr5bULuOCSLZgllcq+eLLC2qh3RKGEf7a11VGFQDYs9ZL+hkJ0uUpnrjLq9VxdASUqCs1uIbbYi/Ku8SqP2IjZtBWN9gQMQAWXV0GNUMlOM7rslY3xmCwqceEO6rsjSf+FRPTuj2i+aR6eiNGpWfZC3CEPnTlW2meO7uXvgtAgK6yNa856ny6vjdWbZ5GwU6YnXcLepdM67/h1QUbn8pXvGqKuHkfifRbeud1MwfMqpdeMLI6rLjDOwt7VkYnncqj5WwyWb0ocvHc0b8I+Xr5GSe05KpKmE1WlBpxIOByuWInqGydSu3jkuOfhkDQIq1foK47B1GniaHkSbX12VLeCx2Wibn8yqQ+Y0fcXj3yxQcgeiCn10peg0LJACDlUkTJS2FyfN+yE2NrSiRT83YkrViKyyl/I7sQwOloi2FBf5OteD6bOGUPydS2UXhFB3hO6n5A94RKKEzbUF9HoDn72iPOMVg7+MgvzjabPLGQ4wRDI6vM1FKdOZI02akGfDGQVzJ0S5i6JsBoTvR8nYCu1Yjtiw9oq0zQzwnjvz2iv54GooyqecJnGxWJDSChz6K5oEn4sE3WHwr/Lp/qltXrC8TTY6Uux0TXBTdMi/4d6xWWQ96JO1B0K/9w/w0/QGhLvlReg5aWjxngpvc5f7G2zPdgbDNsNb0+jzuV/6sorFVOQx+aDBId+NPJBHaNBynrmgRMecWf9R0a1SXRljb7LHVmtEbOxBCSZ9jPz6ck4wcG3FPhmeUkzxrzJ25yYPz6InJJE7fkZqMeJ+T4mZNUiUX+WEPJXBl2i8GknJbcMHCYgtVlIe0+nZsXILVP+XzXKvi4jHdu4oUkUPK1Scv3Iw8XUjSaap0moqQMRXAVPqJRcZzmh2eqR+FRiBshcI6PLEp15yqgns6KOaugSdGed/DMRkrc5aR1vwxv8OGzAGCNHl3vx2mQh5K8imkT+iwPCdcabqV0++i5m7r9Adhti1mXJWDoaJakbTdgbBiZYy64wjWojyInwqcUMkP6Ggsmh0Tre/KmXhT4vJBXiDntwxSg0LBVCFoQen6mJrD1HxRWjGOvQX2IkDRL2uuhLMAkhC0KWz9zfbVjqpSvbQurm3pG//EWgQ8rHDrpyLTSdLmatBaHLSRm8tsz30Dw1/Euxl3so6W930jzNTuscIWRBaHPSZqI6pnmoWxRNxptffLTVMTLXtVB7RjQdU8XOLkHoc/KmlSWd7nEeqlbEkbn2BF64c4rIXF0vjvoRfKU4uWtEko4j10PlJYlf6AEHWS/VUH5NKn3Zomst+Opw8hd8ZZ2+7P4zxT6DoCUVMv98iKx/jPwircFk/aua0m+l40rziDBGwVeKU/NGC9k4U6xyZRKZ61o+9WXU9nb0zhGOJBxE1ks1lH47QxwsIPhKcupeTyPr9OV4qDo/gYy3PvuBa2HN+nHH4pmr6ym7QZwQIvjqcmrfNSXp9BZ4qFscS/q7I79iczgsnTqxz3+MeqiEjA0dAemZa5upvCLVOEVTCFnwFWWUQcWfAUmna5IbkzOClI/7aJg7umNxdQXq7p4PgLVDB/3YK+YH7WfVjXXk6vMSxbnWgq88n9tbINtmeejKtZG4x31SjiCSNEj9qJeG06LpGSuELBB8rq90bTrdgzPWRNwR7wm9F0qXQLJaQZLQLSYkFRJ3u2ibYKdzihCyQABfwPuZ68/yopkkoipVTL2je9mbO1qi9erpKOMLaZwdRdxhDz3pFrFFUyAYxBfysvWaFSqKSydtXTXhdRrWDr1/XBz4XUkzxsy6Al3jYkhbV40rWhFBEwLBED5TPPPJIO9FHWu9sZbcOjMh4OQSxa0Tu8s4xK+3IJaqC0/he1sFgv9iTv1s9ggYpzUY5yONedaB7PY/+cEbaeHQXcfOTxJCFgiG4wsX82BKbjAD5hG/JxAIAvlCxswCgeDkI8QsEIQIQswCQYggxCwQhAhCzAJBiCDELBCECELMAkGIIMQsEIQIQswCQYggxCwQhAhCzAJBiCDELBCECELMAkGIIMQsEIQIQswCQYggxCwQhAhCzAJBiCDELBCECELMAkGIIMQsEIQIQswCQYggxCwQhAhCzAJBiCDELBCECELMAkGIIMQsEIQIQswCQYggxCwQhAhCzAJBiCDELBCECELMAkGIIMQsEIQIQswCQYggxCwQhAhCzAJBiCDELBCECELMAkGIIMQsEIQIQswCQYggxCwQhAhCzAJBiCDELBCECELMAkGIIMQsEIQIQswCQYggxCwQhAhCzAJBiCDELBCECELMAkGIIMQsEIQIQswCQYggxCwQhAhCzAJBiCDELBCECELMAkGIIMQsEIQIQswCQYggxCwQhAhCzAJBiCDELBCECELMAkGIIMQsEIQIQswCQYggxCwQhAhCzAJBiCDELBCECELMAkGIIMQsEIQIQswCQYggxCwQhAhCzAJBiCDELBCECELMAkGIIMQsEIQIQswCQYggxCwQhAhCzAJBiCDELBCECELMAkGIIMQsEIQIQswCQYggxCwQhAhCzAJBiCDELBCECELMAkGIIMQsEIQIQswCQYggxCwQhAhCzAJBiCDELBCECELMAkGIIMQsEIQIQswCQYggxCwQhAimLzoDgv8ezM1m7HUSAD05Gmq015cWedCC7On/Q4LOye4vIIdfbYSYBSNiajMTfQS84RLuKOOzyHKZ7jwT4VUy5h6dviTwhhlpkg7xW8zoJmibqhK/XcETIdE1SQj8VCLELDguSoeJiEqJjiKdibPL+M+YNwGYseMyotfG05uu05sB717+EBmmCJ9dwd9uQlIh8WOFtolgckDKOyYcyTJdE4SoTwVS1jMP6F90JgRfMtwymYZmcSQoxF5Vw4ZxawB4w2FlU/c4ro3dwncOX83vC19irk3hvubxODQLAA8m7wagU+tj1gffpXjR82x2anx903ew1JtJ3qbSXmiiZ6wQ9clEiFngj1cmax20Xt8LQGFCE68UbGCzU+OFlgUUdybR2BlJQWILqWGd/DRlA4+3zmdN5URU1ZhPXZhVxpMZW4Je/j+9Edy770KcTjOqQ3QMTyZCzAJ/3DLjf1PHuq1rqfL28IPqCwBo7osg1d7FlUkfA/Cn6qVUtsZRkNhCZXssPxz3FjFKr+8yf2+ay19z3h32NmPevY6wHXa6x4nW+WQhHo2CATSJsav6GP9qLU1qL/fWnsvtqRv4wFHINj2H+zPWktU/Lh6T9xJt2Ta+X/w1fj5+HReEt2OWFN+lElM3AOagt1ly4EKi3wmjfbz2eZTqK4NomQUGusS437axbPUOro8+xDfKLuKRnJfJNUfQoznp1rykDprgOkaFp4e7jl7MM7mvEqvYR7zNJaVnUfdkPs2zQIvwjvh9wegRYhYAMP7ndXz73fe5KLwHVddoUh1BxRuMem8PSYodRRp5D1K9t4f5b9xB0ocmmk73jPh9wegRO8AEjLvnKD/9YA0XhfcAoEjyqIUMkGqKGJWQj3132/JHaZmmE/9x8G644NMhxCxA6+hkhvXzu1+CEo5u1gd2jAlOCmICTMCRP07h/DwJyWJh/ZEPTum95u25lK4PkiFNPaX3+SoixCwARefIHyeDouPR3/WblT4ZeHQVs6RwWfkZRDwQhXusTkypRN0y0TSfTEQ3W2Ag6eCRuHD6ctpVx0m77Ms9UZx/0XV0an3GBxq0zfZQt0zMZJ9shJgFAyg6h36Rw9VnXMMht4NDbgdV3p5PfbkyTw/PzJtJ2coILvrWrbQ6w1GtosqdKkQ3W+CPWePwLQncNetCADoX5/Gnh/6XqVZjhqxHcyIjY5ctI17qbUchxMXgjfdQd5qZzF8nUP516ZRm/6uMWGcWHBdbjYXwGp3f3fMki8M0Hm3PwSypnBV+mGyTBatkLC+VeXrINw8sZ3VqfVwx9kwO/2HcF5X1rxyizyM4Ls4MN45kidv++F3WOWzcHluJQ7Nwa9llHHIb2zEPuPu4++jFuHQxofVFIsQsGJG+XDdKn86d/7wegLvjynijaJ2v6/2n5iWsynsNq2RmZdmZAFxevBJ16pgvLM9fRcSYWfCZeTz9YyCMeXsuxf67GKbdegVxfwin/HoxPv48ES2zYFT0JUlYOiTyNt7AY+3ZQb8T+/V2yr+mEPV0FOVXCSF/3ggxC0aFM8uNaoXUtRae+NsKnuhI90vPff1btC8rBFmn6iIR2vhFIGazBSeMvcyCrIJ3UMRjeLVO6ywVTELIXxRizCw4YRz5buzlFmxNA58JIX/xCDELPhWOPDcnb9On4GQgxswCQYggxCwQhAhCzAJBiCDELBCECELMAkGIIMQsEIQIQswCQYggxCwQhAhCzAJBiCDELBCECELMAkGIIMQsEIQIQswCQYggxHwKsNVYiN5jQekwgS5O3BB8PpzyEEhzuIeZWVUAHGlLpK0xyi99bF49cVYjmG7LkXwYHBKr6MwrLAeg1RlOcUWKn21iaicFMS0AbDuajdfhX5x548oA8Ooy2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      "text/plain": [
       "<Figure size 1440x1152 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "state, reward, done, info = env.step([env.action_space.sample()])\n",
    "show_img_4(state)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 四、Monitor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from stable_baselines3.common.monitor import Monitor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'DummyVecEnv' object has no attribute 'reward_range'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32mC:\\Users\\RENYIF~1\\AppData\\Local\\Temp/ipykernel_178876/1370986169.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mmonitor_dir\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34mr'./monitor_log/'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0menv\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mMonitor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0menv\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mmonitor_dir\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mD:\\software\\e_anaconda\\envs\\pytorch\\lib\\site-packages\\stable_baselines3\\common\\monitor.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, env, filename, allow_early_resets, reset_keywords, info_keywords)\u001b[0m\n\u001b[0;32m     37\u001b[0m         \u001b[0minfo_keywords\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mTuple\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m...\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     38\u001b[0m     ):\n\u001b[1;32m---> 39\u001b[1;33m         \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mMonitor\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0menv\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0menv\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     40\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mt_start\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     41\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mfilename\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\software\\e_anaconda\\envs\\pytorch\\lib\\site-packages\\gym\\core.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, env)\u001b[0m\n\u001b[0;32m    227\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maction_space\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0menv\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maction_space\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    228\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobservation_space\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0menv\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobservation_space\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 229\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreward_range\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0menv\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreward_range\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    230\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmetadata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0menv\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmetadata\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    231\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\software\\e_anaconda\\envs\\pytorch\\lib\\site-packages\\stable_baselines3\\common\\vec_env\\base_vec_env.py\u001b[0m in \u001b[0;36m__getattr__\u001b[1;34m(self, name)\u001b[0m\n\u001b[0;32m    310\u001b[0m             \u001b[1;32mraise\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0merror_str\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    311\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 312\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetattr_recursive\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    313\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    314\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_get_all_attributes\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[0mDict\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mAny\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\software\\e_anaconda\\envs\\pytorch\\lib\\site-packages\\stable_baselines3\\common\\vec_env\\base_vec_env.py\u001b[0m in \u001b[0;36mgetattr_recursive\u001b[1;34m(self, name)\u001b[0m\n\u001b[0;32m    335\u001b[0m             \u001b[0mattr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvenv\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetattr_recursive\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    336\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m  \u001b[1;31m# attribute not present, child is an unwrapped VecEnv\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 337\u001b[1;33m             \u001b[0mattr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvenv\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    338\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    339\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mattr\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'DummyVecEnv' object has no attribute 'reward_range'"
     ]
    }
   ],
   "source": [
    "monitor_dir = r'./monitor_log/'\n",
    "env = Monitor(env,monitor_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 五、整理引入"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nes_py.wrappers import JoypadSpace\n",
    "import gym_super_mario_bros\n",
    "from gym_super_mario_bros.actions import SIMPLE_MOVEMENT\n",
    "import time\n",
    "from matplotlib import pyplot as plt\n",
    "from gym.wrappers import GrayScaleObservation\n",
    "from stable_baselines3.common.monitor import Monitor\n",
    "from stable_baselines3.common.vec_env import DummyVecEnv\n",
    "from stable_baselines3.common.vec_env import VecFrameStack\n",
    "import os\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "env = gym_super_mario_bros.make('SuperMarioBros-v0')\n",
    "env = JoypadSpace(env, SIMPLE_MOVEMENT)\n",
    "\n",
    "monitor_dir = r'./monitor_log/'\n",
    "os.makedirs(monitor_dir,exist_ok=True)\n",
    "env = Monitor(env,monitor_dir)\n",
    "\n",
    "env = GrayScaleObservation(env,keep_dim=True)\n",
    "env = DummyVecEnv([lambda: env])\n",
    "env = VecFrameStack(env,4,channels_order='last')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 240, 256, 4)\n"
     ]
    }
   ],
   "source": [
    "state = env.reset()\n",
    "print(state.shape)"
   ]
  }
 ],
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  "kernelspec": {
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